Artificial Intelligence in Financial and Supply Chain Optimization: Predictive Analytics for Business Growth and Market Stability in The USA

Опубликована: Март 7, 2025

This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in optimizing supply chain operations financial forecasting USA. The research examines how AI-driven predictive analytics can foster business growth stabilize markets. A diverse set ML models is employed to address various challenges: Long Short-Term Memory (LSTM) networks are used for sequence economic domains, while Logistic Regression, Random Forest, Boosting techniques support fraud detection. Additionally, autoencoders Isolation Forest algorithms applied identify unusual transactions, ARIMA forecast demand spikes seasonality. For logistics optimization, Reinforcement ( Deep Q-Networks) improve route planning, Neural Networks predict optimal restocking periods based on patterns. XGBoost assess customer price sensitivity optimize pricing strategies. performance evaluated using Root Mean Squared Error (RMSE) Absolute Percentage (MAPE). In contrast, detection effectiveness measured through Precision, Recall, F1-score, Area Under Curve (AUC-ROC). Logistics assessed by Total Delivery Time, Cost Reduction, Efficiency Gains predictions validated via accuracy, (MSE), inventory turnover rates. Pricing strategies Revenue Impact, Elasticity Metrics, Customer Retention Rates.

Язык: Английский

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An efficient hybrid Deep Learning-Machine Learning method for diagnosing neurodegenerative disorders.

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 4, 2025

A neurodegenerative illness known as Alzheimer's causes the loss of brain cells and progressive atrophy tissue. It badly affects a person’s normal life. However, if we are able to detect it early treat it, most patients will be recover some degree lead life with dependence. Continuous clinical assessment is needed for diagnosing this type disorder. Medical diagnosis today extensively relies on deep learning approaches. medical image data analysis has lot constraints. One major constraints faced during scarcity imbalance. In light these concerns, current study sets out create hybrid model that can effectively categorise various disease variants using magnetic resonance imaging (MRI) data. For solving imbalance, first, blur sharpen all images, finally, pass images along original through predefined CNN (Convolutional Neural Network) was trained mnist weight extracting features, then features an extra-tree classifier feature reduction, finally input reduced customised model. This work used different pre-trained models our DNN (Deep compared those cutting-edge chosen base The results state proposed model, which ResNet dropout concept, got highest values training accuracy (98.20) validation (92.61). also addresses problem overfitting.

Язык: Английский

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Emerging Trends in Deep Learning for Early Alzheimer's Disease Diagnosis and Classification: A Comprehensive Review

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 4, 2025

Alzheimer's Disease (AD), a progressive neurodegenerative disorder, manifests as cognitive decline and memory loss, significantly impacting individuals' lives healthcare systems globally. Early diagnosis intervention are crucial for improving patient outcomes managing the disease effectively. Recent advancements in deep learning (DL) have shown substantial promise medical image classification early AD diagnosis. This survey evaluates state-of-the-art DL techniques, including hybrid models, Recurrent Neural Networks (RNNs), Convolutional (CNNs), applied across imaging modalities such computed tomography (CT), positron emission (PET), magnetic resonance (MRI). It emphasizes their performance, accuracy, computational efficiency while addressing critical challenges like need large annotated datasets, overfitting, model interpretability. Furthermore, explores how could revolutionize identifies future research directions to bridge existing gaps, aiming improve detection personalized diagnostic approaches individuals with AD.

Язык: Английский

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Diagnosis, visualisation and analysis of COVID-19 using Machine learning

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 4, 2025

The Focal point of this paper is to out or analyse the different kinds symptoms and other complications COVID-19 Positive Negative patients undergo. Coronaviruses are a club viruses that attack humans with respiratory illness their impact ranges from mild cold, fever, dry cough severe breathing problems, fatigue, chest pain some chronic problems. objective research various undergone by patient. By considering most standard (given WHO Ministry Health, govt India), data collected renowned repository called Kaggle employed best analytical techniques clean it so must befits our higher Machine Learning prediction aspirations. In study, Ensemble machine learning models have been used, which take user input on pre-defined approved predict whether present not. developed model cannot be left like this, without any proper interface for duly picking up each users, we managed reach weighted framework termed Streamlit, transforming into fully-fledged dual- faceted (Fill manually going cell directly drop patient in CSV file format) Web Application.

Язык: Английский

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Artificial Intelligence Technique Based Effective Disaster Recovery Framework to Provide Longer Time Connectivity in Mobile Ad-hoc Networks

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 4, 2025

Communication plays a vital role for effective management and the execution of disaster response emergency recovery efforts must be able to exchange information with each other from anywhere, at any time successfully fulfill their missions. Therefore, it is important configure communications networks in conditions using ad-hoc networks. This proposed framework collects communication before or after disaster. The aim this research work propose possible practical model by network configuration technologies Greedy Randomized Adaptive Search Procedure (GRASP) algorithm. development improve facilitate coordination among services field offices, state/level entities private industry. accomplished integration existing systems, implementation new efficient interconnection established artificial based techniques

Язык: Английский

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Artificial intelligence contributes to the creative transformation and innovative development of traditional Chinese culture

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 14, 2025

In recent years, artificial intelligence (AI) has emerged as a transformative force in various fields, including the arts and culture. This is particularly evident context of traditional Chinese culture, where AI become powerful tool its creative transformation innovative development. With advanced capabilities data processing generating new ideas, not only helping to preserve rich heritage culture but also playing crucial role evolution. study aims delve into how reshaping elements such calligraphy, paintings artworks, assess impact on both conservation modern reinterpretation. We examine real-world applications projects that utilize technologies, machine learning, natural language processing, computer vision. Our findings indicate AI's contribution multifaceted. One key areas made significant preservation restoration cultural artifacts. algorithms have demonstrated remarkable proficiency analyzing large datasets historical texts uncovering previously unknown patterns facilitating ancient relics. The integration realm signifies pivotal moment history. extends beyond mere preservation; it catalyst for innovation, fostering forms artistic expression promoting dynamic cross-cultural exchange. As technology continues evolve, expected further revolutionize way we interact with understand opening up avenues exploration dialogue. underscores potential enrichment highlights exciting prospects future developments this area.

Язык: Английский

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Towards Smarter E-Learning: Real-Time Analytics and Machine Learning for Personalized Education

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 2, 2025

E-Learning platforms change fast, and real-time behavioural analytics with machine learning provides the most powerful means to enhance learner outcomes. The datasets undergo preprocessing techniques like Z-score outlier detection, Min-Max scaling for feature normalization, Ridge-RFE (Ridge regression Recursive Feature Elimination) selection in order improve accuracy reliability of predictions. Applying Gradient Boosting Machine, classification up a 94% level respect model about predictions on outcomes was achievable. Thus, applying this, feedback systems may offer timely recommendations or directions class that propel students toward better understanding how raise participation success percentages. However, this approach has some potential benefits but there are still various challenges such as managing data imbalance models generalize dynamic environment. Though hybrid methods mitigate problem, pipelines behaviour incorporation call significant computer-intensive resources infrastructure. This integration very high paybacks. It makes possible more responsive individual needs almost met manners, thus giving instantaneous feedback, content suggestions, interventions. Finally, convergence ML culminates adaptive environments which student engagement, retention, quality academic results.

Язык: Английский

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Innovative Computational Intelligence Frameworks for Complex Problem Solving and Optimization

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 9, 2025

The rapid advancement of computational intelligence (CI) techniques has enabled the development highly efficient frameworks for solving complex optimization problems across various domains, including engineering, healthcare, and industrial systems. This paper presents innovative that integrate advanced algorithms such as Quantum-Inspired Evolutionary Algorithms (QIEA), Hybrid Metaheuristics, Deep Learning-based models. These aim to address challenges by improving convergence rates, solution accuracy, efficiency. In context a framework was successfully used predict optimal treatment plans cancer patients, achieving 92% accuracy rate in classification tasks. proposed demonstrate potential addressing broad spectrum problems, from resource allocation smart grids dynamic scheduling manufacturing integration cutting-edge CI methods offers promising future optimizing performance real-world wide range industries.

Язык: Английский

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Metaheuristic-Driven Optimization for Efficient Resource Allocation in Cloud Environments

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 7, 2025

Intrusion Detection Systems (IDS) play a pivotal role in safeguarding networks against evolving cyber threats. This research focuses on enhancing the performance of IDS using deep learning models, specifically XAI, LSTM, CNN, and GRU, evaluated NSL-KDD dataset. The dataset addresses limitations earlier benchmarks by eliminating redundancies balancing classes. A robust preprocessing pipeline, including normalization, one-hot encoding, feature selection, was employed to optimize model inputs. Performance metrics such as Precision, Recall, F1-Score, Accuracy were used evaluate models across five attack categories: DoS, Probe, R2L, U2R, Normal. Results indicate that XAI consistently outperformed other achieving highest accuracy (91.2%) Precision (91.5%) post-BAT optimization. Comparative analyses confusion matrices protocol distributions revealed dominance DoS attacks highlighted specific challenges with R2L U2R study demonstrates effectiveness optimized detecting complex attacks, paving way for adaptive solutions.

Язык: Английский

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Electronic Components Detection Using Various Deep Learning Based Neural Network Models

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 22, 2025

Electronic components of different sizes and types can be used in microelectronics, nanoelectronics, medical electronics, optoelectronics. For this reason, accurate detection all electronic such as transistors, capacitors, resistors, light-emitting diodes chips is great importance. purpose, study, an open source dataset was for the five components. In order to increase amount dataset, firstly, data augmentation processes were performed by rotating component images at certain angles right left directions. After these processes, multi-class classifications using deep learning based neural network models, namely Vision Transformer, MobileNetV2, EfficientNet, Swin Transformer Data-efficient Image Transformer. As a result with various necessary evaluation metrics precision, recall, f1-score accuracy obtained each model, highest value 0.992 model.

Язык: Английский

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